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Odometry-Based Viterbi Localization with Artificial Neural Networks and Laser Range Finders for Mobile Robots

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Abstract

This paper proposes an approach that solves the Robot Localization problem by using a conditional state-transition Hidden Markov Model (HMM). Through the use of Self Organized Maps (SOMs) a Tolerant Observation Model (TOM) is built, while odometer-dependent transition probabilities are used for building an Odometer-Dependent Motion Model (ODMM). By using the Viterbi Algorithm and establishing a trigger value when evaluating the state-transition updates, the presented approach can easily take care of Position Tracking (PT), Global Localization (GL) and Robot Kidnapping (RK) with an ease of implementation difficult to achieve in most of the state-of-the-art localization algorithms. Also, an optimization is presented to allow the algorithm to run in standard microprocessors in real time, without the need of huge probability gridmaps.

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Authors and Affiliations

  1. Universidad Nacional Autónoma de México UNAM, Ciudad Universitaria no. 3000, Col. Copilco Universidad, Del. Coyoacán, México, D.F., C.P. 04360, México

    Adalberto Llarena, Jesus Savage & Boris Escalante-Ramírez

  2. Instituto Tecnológico Autónomo de México ITAM, Río Hondo No. 1, Col. Progreso Tizapán, México, D.F., C.P. 01080, México

    Angel Kuri

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  1. Adalberto Llarena
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  2. Jesus Savage
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  3. Angel Kuri
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  4. Boris Escalante-Ramírez
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Correspondence to Adalberto Llarena.

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Llarena, A., Savage, J., Kuri, A. et al. Odometry-Based Viterbi Localization with Artificial Neural Networks and Laser Range Finders for Mobile Robots. J Intell Robot Syst 66, 75–109 (2012). https://doi.org/10.1007/s10846-011-9627-8

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